Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations986
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory442.4 KiB
Average record size in memory459.4 B

Variable types

Text1
Categorical11
Numeric10

Alerts

ActivityLevel is highly overall correlated with HasCrCard and 1 other fieldsHigh correlation
Age is highly overall correlated with AgeBandHigh correlation
AgeBand is highly overall correlated with AgeHigh correlation
Balance is highly overall correlated with BalancePerProduct and 4 other fieldsHigh correlation
BalancePerProduct is highly overall correlated with Balance and 4 other fieldsHigh correlation
BalanceToSalaryRatio is highly overall correlated with Balance and 3 other fieldsHigh correlation
CustomerRiskScore is highly overall correlated with IsActiveMember and 1 other fieldsHigh correlation
EstimatedSalary is highly overall correlated with HighValueCustomer and 2 other fieldsHigh correlation
HasCrCard is highly overall correlated with ActivityLevelHigh correlation
HasZeroBalance is highly overall correlated with Balance and 3 other fieldsHigh correlation
HighValueCustomer is highly overall correlated with Balance and 4 other fieldsHigh correlation
IsActiveMember is highly overall correlated with ActivityLevel and 1 other fieldsHigh correlation
IsSingleProduct is highly overall correlated with CustomerRiskScore and 2 other fieldsHigh correlation
LogBalance is highly overall correlated with Balance and 4 other fieldsHigh correlation
LogSalary is highly overall correlated with EstimatedSalary and 2 other fieldsHigh correlation
NumOfProducts is highly overall correlated with IsSingleProductHigh correlation
SalaryPerProduct is highly overall correlated with EstimatedSalary and 2 other fieldsHigh correlation
Tenure is highly overall correlated with TenureBandHigh correlation
TenureBand is highly overall correlated with TenureHigh correlation
CustomerID has unique values Unique
Balance has 475 (48.2%) zeros Zeros
BalancePerProduct has 475 (48.2%) zeros Zeros
BalanceToSalaryRatio has 475 (48.2%) zeros Zeros
CustomerRiskScore has 192 (19.5%) zeros Zeros
LogBalance has 475 (48.2%) zeros Zeros

Reproduction

Analysis started2025-08-07 08:48:50.514740
Analysis finished2025-08-07 08:49:11.544793
Duration21.03 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CustomerID
Text

Unique 

Distinct986
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
2025-08-07T14:19:11.872305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters7888
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique986 ?
Unique (%)100.0%

Sample

1st rowCUST0001
2nd rowCUST0002
3rd rowCUST0003
4th rowCUST0004
5th rowCUST0005
ValueCountFrequency (%)
cust0011 1
 
0.1%
cust1000 1
 
0.1%
cust0001 1
 
0.1%
cust0002 1
 
0.1%
cust0003 1
 
0.1%
cust0004 1
 
0.1%
cust0005 1
 
0.1%
cust0006 1
 
0.1%
cust0007 1
 
0.1%
cust0008 1
 
0.1%
Other values (976) 976
99.0%
2025-08-07T14:19:12.417279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1282
16.3%
C 986
12.5%
U 986
12.5%
S 986
12.5%
T 986
12.5%
1 298
 
3.8%
9 297
 
3.8%
5 296
 
3.8%
7 296
 
3.8%
4 296
 
3.8%
Other values (4) 1179
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1282
16.3%
C 986
12.5%
U 986
12.5%
S 986
12.5%
T 986
12.5%
1 298
 
3.8%
9 297
 
3.8%
5 296
 
3.8%
7 296
 
3.8%
4 296
 
3.8%
Other values (4) 1179
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1282
16.3%
C 986
12.5%
U 986
12.5%
S 986
12.5%
T 986
12.5%
1 298
 
3.8%
9 297
 
3.8%
5 296
 
3.8%
7 296
 
3.8%
4 296
 
3.8%
Other values (4) 1179
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1282
16.3%
C 986
12.5%
U 986
12.5%
S 986
12.5%
T 986
12.5%
1 298
 
3.8%
9 297
 
3.8%
5 296
 
3.8%
7 296
 
3.8%
4 296
 
3.8%
Other values (4) 1179
14.9%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
female
513 
male
473 

Length

Max length6
Median length6
Mean length5.040568
Min length4

Characters and Unicode

Total characters4970
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 513
52.0%
male 473
48.0%

Length

2025-08-07T14:19:12.616318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:12.763229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 513
52.0%
male 473
48.0%

Most occurring characters

ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
m 986
19.8%
l 986
19.8%
f 513
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
m 986
19.8%
l 986
19.8%
f 513
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
m 986
19.8%
l 986
19.8%
f 513
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
m 986
19.8%
l 986
19.8%
f 513
 
10.3%

Age
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.631846
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:12.941042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median43
Q356
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.342432
Coefficient of variation (CV)0.3516338
Kurtosis-1.1719129
Mean43.631846
Median Absolute Deviation (MAD)13
Skewness0.041282039
Sum43021
Variance235.39021
MonotonicityNot monotonic
2025-08-07T14:19:13.144945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 33
 
3.3%
53 27
 
2.7%
26 25
 
2.5%
20 25
 
2.5%
39 25
 
2.5%
54 25
 
2.5%
65 24
 
2.4%
21 23
 
2.3%
70 23
 
2.3%
34 23
 
2.3%
Other values (43) 733
74.3%
ValueCountFrequency (%)
18 17
1.7%
19 19
1.9%
20 25
2.5%
21 23
2.3%
22 17
1.7%
23 17
1.7%
24 20
2.0%
25 20
2.0%
26 25
2.5%
27 15
1.5%
ValueCountFrequency (%)
70 23
2.3%
69 21
2.1%
68 13
1.3%
67 13
1.3%
66 23
2.3%
65 24
2.4%
64 21
2.1%
63 17
1.7%
62 16
1.6%
61 11
1.1%

Tenure
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5496957
Minimum0
Maximum20
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:13.304589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0096402
Coefficient of variation (CV)0.54230724
Kurtosis0.64678699
Mean5.5496957
Median Absolute Deviation (MAD)2
Skewness0.38327724
Sum5472
Variance9.0579339
MonotonicityNot monotonic
2025-08-07T14:19:13.640901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 109
11.1%
6 107
10.9%
3 101
10.2%
2 97
9.8%
1 96
9.7%
4 96
9.7%
10 96
9.7%
7 94
9.5%
9 92
9.3%
5 91
9.2%
Other values (2) 7
 
0.7%
ValueCountFrequency (%)
0 3
 
0.3%
1 96
9.7%
2 97
9.8%
3 101
10.2%
4 96
9.7%
5 91
9.2%
6 107
10.9%
7 94
9.5%
8 109
11.1%
9 92
9.3%
ValueCountFrequency (%)
20 4
 
0.4%
10 96
9.7%
9 92
9.3%
8 109
11.1%
7 94
9.5%
6 107
10.9%
5 91
9.2%
4 96
9.7%
3 101
10.2%
2 97
9.8%

Balance
Real number (ℝ)

High correlation  Zeros 

Distinct506
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53176.171
Minimum0
Maximum276057.43
Zeros475
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:13.815991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6236.655
Q3110346.18
95-th percentile183576.45
Maximum276057.43
Range276057.43
Interquartile range (IQR)110346.18

Descriptive statistics

Standard deviation66524.858
Coefficient of variation (CV)1.2510276
Kurtosis-0.66922733
Mean53176.171
Median Absolute Deviation (MAD)6236.655
Skewness0.86874478
Sum52431705
Variance4.4255567 × 109
MonotonicityNot monotonic
2025-08-07T14:19:14.014531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
48.2%
6708 6
 
0.6%
276057.425 2
 
0.2%
1154.97 1
 
0.1%
77109.94 1
 
0.1%
48964.07 1
 
0.1%
37264.98 1
 
0.1%
155251.43 1
 
0.1%
104646.02 1
 
0.1%
193085.81 1
 
0.1%
Other values (496) 496
50.3%
ValueCountFrequency (%)
0 475
48.2%
1154.97 1
 
0.1%
2023.89 1
 
0.1%
2031.79 1
 
0.1%
2040.69 1
 
0.1%
2280.61 1
 
0.1%
2545.56 1
 
0.1%
2715.18 1
 
0.1%
3272.18 1
 
0.1%
3312.49 1
 
0.1%
ValueCountFrequency (%)
276057.425 2
0.2%
199883.33 1
0.1%
199365.03 1
0.1%
198935.08 1
0.1%
198659.77 1
0.1%
198657.75 1
0.1%
198370.18 1
0.1%
198303.59 1
0.1%
198194.97 1
0.1%
198081.99 1
0.1%

NumOfProducts
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
1
256 
2
250 
4
247 
3
233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row1
4th row2
5th row4

Common Values

ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

Length

2025-08-07T14:19:14.194426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:14.342567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

Most occurring characters

ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 256
26.0%
2 250
25.4%
4 247
25.1%
3 233
23.6%

HasCrCard
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
1
513 
0
473 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

Length

2025-08-07T14:19:14.511152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:14.663584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

Most occurring characters

ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 513
52.0%
0 473
48.0%

IsActiveMember
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
527 
1
459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

Length

2025-08-07T14:19:14.830878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:14.979720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

Most occurring characters

ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 527
53.4%
1 459
46.6%

EstimatedSalary
Real number (ℝ)

High correlation 

Distinct981
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83751.967
Minimum20084.94
Maximum149913.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:15.162796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20084.94
5-th percentile26568.095
Q149849.145
median83351
Q3115618.37
95-th percentile143691.02
Maximum149913.72
Range129828.78
Interquartile range (IQR)65769.227

Descriptive statistics

Standard deviation37861.321
Coefficient of variation (CV)0.45206486
Kurtosis-1.2033725
Mean83751.967
Median Absolute Deviation (MAD)32519.19
Skewness0.048659573
Sum82579439
Variance1.4334796 × 109
MonotonicityNot monotonic
2025-08-07T14:19:15.378673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83351 6
 
0.6%
131792.25 1
 
0.1%
47209.79 1
 
0.1%
83027.9 1
 
0.1%
68324.51 1
 
0.1%
113323.18 1
 
0.1%
99514.91 1
 
0.1%
128315.34 1
 
0.1%
47032.42 1
 
0.1%
97727 1
 
0.1%
Other values (971) 971
98.5%
ValueCountFrequency (%)
20084.94 1
0.1%
20203.46 1
0.1%
20269.95 1
0.1%
20302.04 1
0.1%
20304.12 1
0.1%
20829.28 1
0.1%
21213.11 1
0.1%
21277.58 1
0.1%
21311.78 1
0.1%
21336.98 1
0.1%
ValueCountFrequency (%)
149913.72 1
0.1%
149633.71 1
0.1%
149471.06 1
0.1%
149432.04 1
0.1%
149291.57 1
0.1%
149230.62 1
0.1%
148977.33 1
0.1%
148851.92 1
0.1%
148737.07 1
0.1%
148534.74 1
0.1%

Churn
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
519 
1
467 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

Length

2025-08-07T14:19:15.564226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:15.717380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

Most occurring characters

ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 519
52.6%
1 467
47.4%

BalancePerProduct
Real number (ℝ)

High correlation  Zeros 

Distinct510
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17266.209
Minimum0
Maximum99682.515
Zeros475
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:15.899908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1652.913
Q330996.896
95-th percentile66097.823
Maximum99682.515
Range99682.515
Interquartile range (IQR)30996.896

Descriptive statistics

Standard deviation23740.637
Coefficient of variation (CV)1.3749768
Kurtosis1.2880866
Mean17266.209
Median Absolute Deviation (MAD)1652.913
Skewness1.4086447
Sum17024482
Variance5.6361783 × 108
MonotonicityNot monotonic
2025-08-07T14:19:16.117830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
48.2%
1341.6 2
 
0.2%
3354 2
 
0.2%
51750.47667 1
 
0.1%
20929.204 1
 
0.1%
38617.162 1
 
0.1%
11990.25 1
 
0.1%
62623.23 1
 
0.1%
12330.294 1
 
0.1%
37727.806 1
 
0.1%
Other values (500) 500
50.7%
ValueCountFrequency (%)
0 475
48.2%
505.9725 1
 
0.1%
543.036 1
 
0.1%
577.485 1
 
0.1%
680.23 1
 
0.1%
726.506 1
 
0.1%
848.52 1
 
0.1%
997.048 1
 
0.1%
1015.895 1
 
0.1%
1033.178 1
 
0.1%
ValueCountFrequency (%)
99682.515 1
0.1%
99328.875 1
0.1%
98473.12 1
0.1%
97760.88 1
0.1%
95604.535 1
0.1%
95307.02 1
0.1%
94756.35 1
0.1%
94468.77 1
0.1%
94074.625 1
0.1%
93861.33 1
0.1%

AgeBand
Categorical

High correlation 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
18-29
229 
60+
196 
30-39
193 
40-49
186 
50-59
182 

Length

Max length5
Median length5
Mean length4.6024341
Min length3

Characters and Unicode

Total characters4538
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50-59
2nd row18-29
3rd row40-49
4th row40-49
5th row60+

Common Values

ValueCountFrequency (%)
18-29 229
23.2%
60+ 196
19.9%
30-39 193
19.6%
40-49 186
18.9%
50-59 182
18.5%

Length

2025-08-07T14:19:16.313264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:16.473314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18-29 229
23.2%
60 196
19.9%
30-39 193
19.6%
40-49 186
18.9%
50-59 182
18.5%

Most occurring characters

ValueCountFrequency (%)
- 790
17.4%
9 790
17.4%
0 757
16.7%
3 386
8.5%
4 372
8.2%
5 364
8.0%
1 229
 
5.0%
8 229
 
5.0%
2 229
 
5.0%
6 196
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 790
17.4%
9 790
17.4%
0 757
16.7%
3 386
8.5%
4 372
8.2%
5 364
8.0%
1 229
 
5.0%
8 229
 
5.0%
2 229
 
5.0%
6 196
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 790
17.4%
9 790
17.4%
0 757
16.7%
3 386
8.5%
4 372
8.2%
5 364
8.0%
1 229
 
5.0%
8 229
 
5.0%
2 229
 
5.0%
6 196
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 790
17.4%
9 790
17.4%
0 757
16.7%
3 386
8.5%
4 372
8.2%
5 364
8.0%
1 229
 
5.0%
8 229
 
5.0%
2 229
 
5.0%
6 196
 
4.3%

TenureBand
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
Long (6+ years)
502 
Medium (3-5 years)
288 
New (0-2 years)
196 

Length

Max length18
Median length15
Mean length15.876268
Min length15

Characters and Unicode

Total characters15654
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium (3-5 years)
2nd rowLong (6+ years)
3rd rowLong (6+ years)
4th rowNew (0-2 years)
5th rowMedium (3-5 years)

Common Values

ValueCountFrequency (%)
Long (6+ years) 502
50.9%
Medium (3-5 years) 288
29.2%
New (0-2 years) 196
 
19.9%

Length

2025-08-07T14:19:16.662715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:16.829661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
years 986
33.3%
long 502
17.0%
6 502
17.0%
medium 288
 
9.7%
3-5 288
 
9.7%
new 196
 
6.6%
0-2 196
 
6.6%

Most occurring characters

ValueCountFrequency (%)
1972
 
12.6%
e 1470
 
9.4%
y 986
 
6.3%
( 986
 
6.3%
r 986
 
6.3%
a 986
 
6.3%
) 986
 
6.3%
s 986
 
6.3%
o 502
 
3.2%
L 502
 
3.2%
Other values (16) 5292
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1972
 
12.6%
e 1470
 
9.4%
y 986
 
6.3%
( 986
 
6.3%
r 986
 
6.3%
a 986
 
6.3%
) 986
 
6.3%
s 986
 
6.3%
o 502
 
3.2%
L 502
 
3.2%
Other values (16) 5292
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1972
 
12.6%
e 1470
 
9.4%
y 986
 
6.3%
( 986
 
6.3%
r 986
 
6.3%
a 986
 
6.3%
) 986
 
6.3%
s 986
 
6.3%
o 502
 
3.2%
L 502
 
3.2%
Other values (16) 5292
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1972
 
12.6%
e 1470
 
9.4%
y 986
 
6.3%
( 986
 
6.3%
r 986
 
6.3%
a 986
 
6.3%
) 986
 
6.3%
s 986
 
6.3%
o 502
 
3.2%
L 502
 
3.2%
Other values (16) 5292
33.8%

ActivityLevel
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.6 KiB
Medium
508 
Low
246 
High
232 

Length

Max length6
Median length6
Mean length4.7809331
Min length3

Characters and Unicode

Total characters4714
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowMedium
3rd rowMedium
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 508
51.5%
Low 246
24.9%
High 232
23.5%

Length

2025-08-07T14:19:17.025142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:17.195963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 508
51.5%
low 246
24.9%
high 232
23.5%

Most occurring characters

ValueCountFrequency (%)
i 740
15.7%
M 508
10.8%
e 508
10.8%
d 508
10.8%
u 508
10.8%
m 508
10.8%
L 246
 
5.2%
o 246
 
5.2%
w 246
 
5.2%
H 232
 
4.9%
Other values (2) 464
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 740
15.7%
M 508
10.8%
e 508
10.8%
d 508
10.8%
u 508
10.8%
m 508
10.8%
L 246
 
5.2%
o 246
 
5.2%
w 246
 
5.2%
H 232
 
4.9%
Other values (2) 464
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 740
15.7%
M 508
10.8%
e 508
10.8%
d 508
10.8%
u 508
10.8%
m 508
10.8%
L 246
 
5.2%
o 246
 
5.2%
w 246
 
5.2%
H 232
 
4.9%
Other values (2) 464
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 740
15.7%
M 508
10.8%
e 508
10.8%
d 508
10.8%
u 508
10.8%
m 508
10.8%
L 246
 
5.2%
o 246
 
5.2%
w 246
 
5.2%
H 232
 
4.9%
Other values (2) 464
9.8%

SalaryPerProduct
Real number (ℝ)

High correlation 

Distinct983
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26939.269
Minimum4040.692
Maximum74956.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:17.397216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4040.692
5-th percentile7276.154
Q114507.909
median24160.594
Q335090.591
95-th percentile62362.548
Maximum74956.86
Range70916.168
Interquartile range (IQR)20582.682

Descriptive statistics

Standard deviation15883.263
Coefficient of variation (CV)0.58959518
Kurtosis0.70081538
Mean26939.269
Median Absolute Deviation (MAD)9899.6545
Skewness1.0106971
Sum26562119
Variance2.5227805 × 108
MonotonicityNot monotonic
2025-08-07T14:19:17.618281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20837.75 2
 
0.2%
27783.66667 2
 
0.2%
16670.2 2
 
0.2%
21619.75333 1
 
0.1%
17081.1275 1
 
0.1%
28330.795 1
 
0.1%
15677.47333 1
 
0.1%
25891.31667 1
 
0.1%
8056.484 1
 
0.1%
5466.702 1
 
0.1%
Other values (973) 973
98.7%
ValueCountFrequency (%)
4040.692 1
0.1%
4053.99 1
0.1%
4242.622 1
0.1%
4267.396 1
0.1%
4366.076 1
0.1%
4408.094 1
0.1%
4412.148 1
0.1%
4440.64 1
0.1%
4474.048 1
0.1%
4764.256 1
0.1%
ValueCountFrequency (%)
74956.86 1
0.1%
74645.785 1
0.1%
74368.535 1
0.1%
74255.1 1
0.1%
73931.87 1
0.1%
73816.915 1
0.1%
73728.31 1
0.1%
73207.3 1
0.1%
73161.285 1
0.1%
73038.225 1
0.1%

BalanceToSalaryRatio
Real number (ℝ)

High correlation  Zeros 

Distinct512
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82660573
Minimum0
Maximum8.3224601
Zeros475
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:17.847665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.068741824
Q31.22014
95-th percentile3.5634843
Maximum8.3224601
Range8.3224601
Interquartile range (IQR)1.22014

Descriptive statistics

Standard deviation1.3289704
Coefficient of variation (CV)1.607744
Kurtosis7.3054678
Mean0.82660573
Median Absolute Deviation (MAD)0.068741824
Skewness2.4613891
Sum815.03325
Variance1.7661623
MonotonicityNot monotonic
2025-08-07T14:19:18.064225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
48.2%
2.466040547 1
 
0.1%
0.01160588292 1
 
0.1%
0.5850826199 1
 
0.1%
0.3815887361 1
 
0.1%
0.792308533 1
 
0.1%
1.588607462 1
 
0.1%
0.8932453148 1
 
0.1%
3.299678074 1
 
0.1%
0.4267479618 1
 
0.1%
Other values (502) 502
50.9%
ValueCountFrequency (%)
0 475
48.2%
0.01160588292 1
 
0.1%
0.01465183672 1
 
0.1%
0.01744979046 1
 
0.1%
0.0228097564 1
 
0.1%
0.02566716048 1
 
0.1%
0.03428156289 1
 
0.1%
0.0344949759 1
 
0.1%
0.04020351231 1
 
0.1%
0.04043359212 1
 
0.1%
ValueCountFrequency (%)
8.322460056 1
0.1%
8.055724273 1
0.1%
7.728020107 1
0.1%
7.318843039 1
0.1%
7.276492497 1
0.1%
7.118953768 1
0.1%
7.081950616 1
0.1%
6.813856124 1
0.1%
6.798856367 1
0.1%
6.780365607 1
0.1%

HighValueCustomer
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
553 
1
433 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

Length

2025-08-07T14:19:18.255978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:18.401031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

Most occurring characters

ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 553
56.1%
1 433
43.9%

CustomerRiskScore
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3362069
Minimum0
Maximum1
Zeros192
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:18.558074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.3
Q30.5
95-th percentile0.7
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.22847411
Coefficient of variation (CV)0.67956403
Kurtosis-0.63043842
Mean0.3362069
Median Absolute Deviation (MAD)0.2
Skewness0.15244348
Sum331.5
Variance0.05220042
MonotonicityNot monotonic
2025-08-07T14:19:18.729876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.3 253
25.7%
0.5 219
22.2%
0 192
19.5%
0.2 138
14.0%
0.6 58
 
5.9%
0.7 55
 
5.6%
0.8 36
 
3.7%
0.4 31
 
3.1%
1 4
 
0.4%
ValueCountFrequency (%)
0 192
19.5%
0.2 138
14.0%
0.3 253
25.7%
0.4 31
 
3.1%
0.5 219
22.2%
0.6 58
 
5.9%
0.7 55
 
5.6%
0.8 36
 
3.7%
1 4
 
0.4%
ValueCountFrequency (%)
1 4
 
0.4%
0.8 36
 
3.7%
0.7 55
 
5.6%
0.6 58
 
5.9%
0.5 219
22.2%
0.4 31
 
3.1%
0.3 253
25.7%
0.2 138
14.0%
0 192
19.5%

HasZeroBalance
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
511 
1
475 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

Length

2025-08-07T14:19:19.065715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:19.213394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

Most occurring characters

ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 511
51.8%
1 475
48.2%

IsSingleProduct
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
730 
1
256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters986
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

Length

2025-08-07T14:19:19.379075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T14:19:19.525746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

Most occurring characters

ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 730
74.0%
1 256
 
26.0%

LogBalance
Real number (ℝ)

High correlation  Zeros 

Distinct506
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8205382
Minimum0
Maximum12.528368
Zeros475
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:19.709064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.7354964
Q311.611386
95-th percentile12.120391
Maximum12.528368
Range12.528368
Interquartile range (IQR)11.611386

Descriptive statistics

Standard deviation5.6592407
Coefficient of variation (CV)0.97228822
Kurtosis-1.958021
Mean5.8205382
Median Absolute Deviation (MAD)3.447487
Skewness-0.030775534
Sum5739.0506
Variance32.027005
MonotonicityNot monotonic
2025-08-07T14:19:19.930761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
48.2%
8.811205188 6
 
0.6%
12.52836781 2
 
0.2%
7.052695097 1
 
0.1%
11.25300044 1
 
0.1%
10.79886247 1
 
0.1%
10.52583613 1
 
0.1%
11.95280765 1
 
0.1%
11.55834825 1
 
0.1%
12.17089516 1
 
0.1%
Other values (496) 496
50.3%
ValueCountFrequency (%)
0 475
48.2%
7.052695097 1
 
0.1%
7.613270657 1
 
0.1%
7.617164513 1
 
0.1%
7.621533175 1
 
0.1%
7.732636613 1
 
0.1%
7.842498708 1
 
0.1%
7.90698176 1
 
0.1%
8.093517268 1
 
0.1%
8.105757294 1
 
0.1%
ValueCountFrequency (%)
12.52836781 2
0.2%
12.20549413 1
0.1%
12.20289776 1
0.1%
12.20073885 1
0.1%
12.19935398 1
0.1%
12.19934381 1
0.1%
12.1978952 1
0.1%
12.19755946 1
0.1%
12.19701157 1
0.1%
12.19644136 1
0.1%

LogSalary
Real number (ℝ)

High correlation 

Distinct981
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.209045
Minimum9.9077753
Maximum11.917822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-08-07T14:19:20.153229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.9077753
5-th percentile10.187504
Q110.816776
median11.330828
Q311.658059
95-th percentile11.875428
Maximum11.917822
Range2.0100465
Interquartile range (IQR)0.84128229

Descriptive statistics

Standard deviation0.53724233
Coefficient of variation (CV)0.047929358
Kurtosis-0.61620485
Mean11.209045
Median Absolute Deviation (MAD)0.38963206
Skewness-0.6394473
Sum11052.118
Variance0.28862932
MonotonicityNot monotonic
2025-08-07T14:19:20.403843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.33082788 6
 
0.6%
11.78898969 1
 
0.1%
10.76237775 1
 
0.1%
11.32694402 1
 
0.1%
11.13203848 1
 
0.1%
11.63800784 1
 
0.1%
11.50807281 1
 
0.1%
11.7622539 1
 
0.1%
10.75861369 1
 
0.1%
11.48994339 1
 
0.1%
Other values (971) 971
98.5%
ValueCountFrequency (%)
9.907775347 1
0.1%
9.913658651 1
0.1%
9.916944106 1
0.1%
9.918525908 1
0.1%
9.91862835 1
0.1%
9.944162976 1
0.1%
9.962421805 1
0.1%
9.965456212 1
0.1%
9.967062172 1
0.1%
9.968243862 1
0.1%
ValueCountFrequency (%)
11.91782188 1
0.1%
11.91595234 1
0.1%
11.91486476 1
0.1%
11.91460368 1
0.1%
11.91366322 1
0.1%
11.91325487 1
0.1%
11.91155614 1
0.1%
11.91071398 1
0.1%
11.90994212 1
0.1%
11.90858088 1
0.1%

Interactions

2025-08-07T14:19:09.268879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:51.635169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:53.444398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:55.452010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:57.327901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.225171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:01.353097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:03.271498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:05.345098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:07.249085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:09.450868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:51.809169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:53.629440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:55.629976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:57.513442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.408153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:01.547864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:03.459291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:05.539051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:07.433400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:09.650849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.002774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:53.816598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:55.829914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:57.713123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.595176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:01.741593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:03.659365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:05.733628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:07.633077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:09.839252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.187485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:54.141693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.008235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:57.899277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.775156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:01.937134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:03.851476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:05.923685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:07.822989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.032246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.362654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:54.329604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.191844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:58.090035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.961305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:02.125416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:04.046699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:06.119029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:08.159286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.225957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.542328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:54.509336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.381021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:58.277545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:00.164852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:02.317979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:04.257652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:06.307874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:08.338723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.421051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.725687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:54.696783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.573976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:58.468241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:00.377484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:02.502238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:04.500980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:06.488704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:08.522184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.609802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:52.912191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:54.877178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.753140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:58.651124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:00.755589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:02.696065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:04.732776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:06.676241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:08.699504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.796820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:53.086487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:55.060948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:56.949358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:58.842062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:00.949876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:02.885137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:04.944840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:06.860773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:08.888527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:10.993244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:53.262647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:55.252809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:57.135676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:18:59.035086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:01.150075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:03.082468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:05.148833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:07.057312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-07T14:19:09.077720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-07T14:19:20.629271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ActivityLevelAgeAgeBandBalanceBalancePerProductBalanceToSalaryRatioChurnCustomerRiskScoreEstimatedSalaryGenderHasCrCardHasZeroBalanceHighValueCustomerIsActiveMemberIsSingleProductLogBalanceLogSalaryNumOfProductsSalaryPerProductTenureTenureBand
ActivityLevel1.0000.0650.0560.0000.0450.0000.2040.4260.0190.0000.6990.0000.0000.6980.0000.0000.0590.0000.0670.0000.000
Age0.0651.0000.9530.0040.0070.0080.026-0.303-0.0050.0000.0990.0000.0000.0510.0360.004-0.0050.0690.011-0.0220.033
AgeBand0.0560.9531.0000.0210.0000.0660.0000.3250.0670.0000.0930.0000.0000.0000.0440.0000.0690.0250.0430.0250.000
Balance0.0000.0040.0211.0000.9790.9670.167-0.0170.0290.0000.0000.8590.6490.0000.0001.0000.0290.0000.0320.0040.016
BalancePerProduct0.0450.0070.0000.9791.0000.9570.1480.0100.0220.0990.0000.8320.5560.0330.3990.9790.0220.2740.0960.0050.000
BalanceToSalaryRatio0.0000.0080.0660.9670.9571.0000.112-0.021-0.1250.0630.0220.6880.4380.0000.0930.967-0.1250.040-0.0910.0100.020
Churn0.2040.0260.0000.1670.1480.1121.0000.2770.1230.0000.0500.1410.1670.3490.1840.1590.0930.1880.0870.2430.258
CustomerRiskScore0.426-0.3030.325-0.0170.010-0.0210.2771.000-0.0240.0000.0220.0320.0000.8170.698-0.017-0.0240.4000.153-0.3510.491
EstimatedSalary0.019-0.0050.0670.0290.022-0.1250.123-0.0241.0000.0700.0670.0000.6220.1060.0000.0291.0000.0000.8070.0100.000
Gender0.0000.0000.0000.0000.0990.0630.0000.0000.0701.0000.0000.0120.0000.0150.0290.0570.0840.0000.0800.0370.041
HasCrCard0.6990.0990.0930.0000.0000.0220.0500.0220.0670.0001.0000.0000.0000.0000.0000.0000.0000.0000.0080.0500.033
HasZeroBalance0.0000.0000.0000.8590.8320.6880.1410.0320.0000.0120.0001.0000.3710.0400.0000.9980.0000.0290.0000.0590.000
HighValueCustomer0.0000.0000.0000.6490.5560.4380.1670.0000.6220.0000.0000.3711.0000.0250.0000.5570.5920.0000.4130.0280.000
IsActiveMember0.6980.0510.0000.0000.0330.0000.3490.8170.1060.0150.0000.0400.0251.0000.0000.0560.1060.0000.0660.0000.000
IsSingleProduct0.0000.0360.0440.0000.3990.0930.1840.6980.0000.0290.0000.0000.0000.0001.0000.0230.0260.9990.5640.0000.000
LogBalance0.0000.0040.0001.0000.9790.9670.159-0.0170.0290.0570.0000.9980.5570.0560.0231.0000.0290.0270.0320.0040.000
LogSalary0.059-0.0050.0690.0290.022-0.1250.093-0.0241.0000.0840.0000.0000.5920.1060.0260.0291.0000.0000.8070.0100.000
NumOfProducts0.0000.0690.0250.0000.2740.0400.1880.4000.0000.0000.0000.0290.0000.0000.9990.0270.0001.0000.3810.0000.029
SalaryPerProduct0.0670.0110.0430.0320.096-0.0910.0870.1530.8070.0800.0080.0000.4130.0660.5640.0320.8070.3811.000-0.0010.000
Tenure0.000-0.0220.0250.0040.0050.0100.243-0.3510.0100.0370.0500.0590.0280.0000.0000.0040.0100.000-0.0011.0000.929
TenureBand0.0000.0330.0000.0160.0000.0200.2580.4910.0000.0410.0330.0000.0000.0000.0000.0000.0000.0290.0000.9291.000

Missing values

2025-08-07T14:19:11.254926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-07T14:19:11.474130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryChurnBalancePerProductAgeBandTenureBandActivityLevelSalaryPerProductBalanceToSalaryRatioHighValueCustomerCustomerRiskScoreHasZeroBalanceIsSingleProductLogBalanceLogSalary
0CUST0001male5640.0040040282.4210.00000050-59Medium (3-5 years)Low8056.4840000.00000000.3100.00000010.603695
1CUST0002female28867408.0140127333.51013481.60200018-29Long (6+ years)Medium5466.7020002.46604100.20011.11853410.215905
2CUST0003female4761154.9710199514.911577.48500040-49Long (6+ years)Medium49757.4550000.01160600.2017.05269511.508073
3CUST0004male4210.00211146588.2200.00000040-49New (0-2 years)High48862.7400000.00000010.3100.00000011.895390
4CUST0005male64377109.94400131792.25015421.98800060+Medium (3-5 years)Low26358.4500000.58508310.30011.25300011.788990
5CUST0006male2670.0041170104.1510.00000018-29Long (6+ years)High14020.8300000.00000000.2100.00000011.157752
6CUST0007male19448964.07310128315.34112241.01750018-29Medium (3-5 years)Medium32078.8350000.38158910.50010.79886211.762254
7CUST0008male34437264.9821147032.42012421.66000030-39Medium (3-5 years)High15677.4733330.79230900.20010.52583610.758614
8CUST0009female462155251.4321197727.00051750.47666740-49New (0-2 years)High32575.6666671.58860710.30011.95280811.489943
9CUST0010male257104646.02401117151.61020929.20400018-29Long (6+ years)Medium23430.3220000.89324510.20011.55834811.671233
CustomerIDGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryChurnBalancePerProductAgeBandTenureBandActivityLevelSalaryPerProductBalanceToSalaryRatioHighValueCustomerCustomerRiskScoreHasZeroBalanceIsSingleProductLogBalanceLogSalary
976CUST0991female53784880.7331173876.43021220.18250050-59Long (6+ years)High18469.1075001.14894000.00011.34901411.210163
977CUST0992male47821938.69310124749.3105484.67250040-49Long (6+ years)Medium31187.3275000.17586110.3009.99605311.734069
978CUST0993female24683412.17110149913.72141706.08500018-29Long (6+ years)Medium74956.8600000.55639710.70111.33156111.917822
979CUST0994female247155398.59411144152.98031079.71800018-29Long (6+ years)High28830.5960001.07800410.20011.95375511.878637
980CUST0995male3070.0041090407.6010.00000030-39Long (6+ years)Medium18081.5200000.00000000.5100.00000011.412095
981CUST0996female678101240.1721063987.82033746.72333360+Long (6+ years)Medium21329.2733331.58215400.30011.52526111.066464
982CUST0997female6590.00100145822.3910.00000060+Long (6+ years)Low72911.1950000.00000010.5110.00000011.890152
983CUST0998male2290.0030186086.2100.00000018-29Long (6+ years)Medium21521.5525000.00000000.2100.00000011.363116
984CUST0999male565166805.14210114544.90055601.71333350-59Medium (3-5 years)Medium38181.6333331.45623010.30012.02458811.648731
985CUST1000female341089147.2520177673.95029715.75000030-39Long (6+ years)Medium25891.3166671.14769600.20011.39805611.260288